{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "from sklearn.datasets import load_digits\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optical Recognition of Handwritten Digits Data Set\n",
      "===================================================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 5620\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      "References\n",
      "----------\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(digits.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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G09i9e/e2phOKiooKVVVVxeLfhzn91+7eBDsAyzStqakxjb1lyxZTfEZGRuDYPn36mMYu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\nU0m4FDBCggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9faa0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
    "for i in range(36):\n",
    "    ax = fig.add_subplot(6, 6, i+1, xticks=[], yticks=[])\n",
    "    ax.imshow(digits.images[i],cmap=plt.cm.binary,interpolation='nearest')\n",
    "    ax.text(0, 7, str(digits.target[i]), color=\"red\", fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   5., ...,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0., ...,  10.,   0.,   0.],\n",
       "       [  0.,   0.,   0., ...,  16.,   9.,   0.],\n",
       "       ..., \n",
       "       [  0.,   0.,   1., ...,   6.,   0.,   0.],\n",
       "       [  0.,   0.,   2., ...,  12.,   0.,   0.],\n",
       "       [  0.,   0.,  10., ...,  12.,   1.,   0.]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(digits.data)\n",
    "X_scaled = scaler.transform(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        , -0.33501649, -0.04308102, ..., -1.14664746,\n",
       "        -0.5056698 , -0.19600752],\n",
       "       [ 0.        , -0.33501649, -1.09493684, ...,  0.54856067,\n",
       "        -0.5056698 , -0.19600752],\n",
       "       [ 0.        , -0.33501649, -1.09493684, ...,  1.56568555,\n",
       "         1.6951369 , -0.19600752],\n",
       "       ..., \n",
       "       [ 0.        , -0.33501649, -0.88456568, ..., -0.12952258,\n",
       "        -0.5056698 , -0.19600752],\n",
       "       [ 0.        , -0.33501649, -0.67419451, ...,  0.8876023 ,\n",
       "        -0.5056698 , -0.19600752],\n",
       "       [ 0.        , -0.33501649,  1.00877481, ...,  0.8876023 ,\n",
       "        -0.26113572, -0.19600752]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n",
       "       beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(30, 30, 30), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(30,30,30), activation='logistic', max_iter = 1000)\n",
    "mlp.fit(X_scaled,digits.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted = mlp.predict(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OJu0+ezawZw+enDIF6S42yu4zZgCHD2PmnDk4bGCOyhDhGk0JCQmi9hekWmNw+jijvjtF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa54b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = mlp.predict(X_scaled)\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
    "for i in range(36):\n",
    "    ax = fig.add_subplot(6, 6, i+1, xticks=[], yticks=[])\n",
    "    ax.imshow(digits.images[i],cmap=plt.cm.binary,interpolation='nearest')\n",
    "    ax.text(0, 7, str('{}-{}'.format(digits.target[i],predicted[i])), color=\"red\", fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = []\n",
    "for i, j in zip(digits.target,predicted):\n",
    "    res.append(i==j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99944351697273237"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(res) / len(digits.target) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
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